There is an increasing demand for Unmanned Aerial Vehicles (UAVs) to enter civilian airspace. This raises important flight certification issues. These are defined by the Civil Aviation Authority (CAA) for the United Kingdom in the directorate of airspace policy. The key objective is for UAVs to demonstrate an equivalent level of safety to that of manned aircraft. An important issue is to enable UAVs to use alternative landing sites as a result of an onboard fault or failure, which causes the planned landing site to be out of reach. The CAA states that they, “may think fit to apply . . . a prohibition on flight unless the aircraft is equipped with a mechanism that will cause the said aircraft to land in the event of a failure . . . ”. Currently UAVs on military missions typically use either parachutes to land in an unknown position or flight termination systems including remote detonation where the UAV does not attempt to land.
In the case of manned aircraft, during emergencies pilots use a combination of local geographical knowledge and visual observations to select an alternative site. This is evident in real-life examples such as US Airways flight 1549, an Airbus A320 jet. The piloted aircraft was successfully ditched in the Hudson river in 2009 after bird strike during climb out.
One of the earliest autonomous aircraft landings using computer vision was demonstrated in 2002. A heliport landing site was detected and tracked in images, using appearance based information using a camera fixed perpendicular to the ground. By low pass filtering, binarising and performing connected component labelling, a set of candidates regions was determined. Object recognition was performed by calculating geometric information such as perimeter, area and moments for each candidate region. Experiments used a small scale remote control helicopter with differential-GPS.
Fixed-wing miniature air vehicles have also been used to research flight stability. Such vehicles typically use image processing to detect the position of the horizon line in the image. This relies upon recognising the appearance of sky and ground terrain using colour information. However, this image based solution requires that the detected horizon line also measures the aircraft's orientation with respect to the ground. Traditionally, these measurements were independent of any onboard positioning sensors.
A survey paper by Gong and Abbotty particularly emphasises the use of computer vision techniques for detecting and tracking airport runways. Some of these techniques rely on model-matching. The objective of model-matching is to estimate the position and orientation of the modelled object with respect to the local coordinate frame. Autoland is a commercial system, which matches a geometric model of a predefined target to image features.
A model-matching approach has also been used for terrain aided localisation using electro-optical sensing. Neither solution relies on GPS or onboard pose information.
Algorithms have been proposed which use an estimate of the imaging projective transform parameters to project points of a prior model into the image. These points are then associated with feature points extracted in the image. The horizontal and vertical pixel errors between each point correspondence are then expressed by two equations. These use the sum of the products of its partial derivatives multiplied by an unknown multi-dimensional vector of corrections to the parameterisation of the imaging projective transform. The process is iterated until convergence or a maximum number of iterations is reached.
Other techniques include estimating the optical flow between frames. This approach has been used to automate the landing of miniature aircraft. Further, in 2007 the automatic detection and selection of regions in an image that could be designated as suitable for landing was successfully demonstrated. This approach required no prior landing site position information, but relied on having good quality imagery of the terrain at the appropriate scale. Furthermore the camera was restricted to being perpendicular to the ground plane and the solution provided no geospatial referencing of the chosen region to the global coordinate frame.
Research into the same problem has also been performed using a downward-scanning LADAR to determine terrain slope, roughness and obstacles for identifying a landing site.